Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Super-efficient Echocardiography Video Segmentation via Proxy- and Kernel-Based Semi-supervised Learning
Authors: Huisi Wu, Jingyin Lin, Wende Xie, Jing Qin
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments have been conducted on two famous public echocardiography video datasets, Echo Net-Dynamic and CAMUS. Our model achieves the best performance-efficiency trade-off when compared with other state-of-the-art approaches, attaining comparative accuracy with a much faster speed. |
| Researcher Affiliation | Academia | Huisi Wu1*, Jingyin Lin1, Wende Xie1, Jing Qin2 1 College of Computer Science and Software Engineering, Shenzhen University 2 Centre for Smart Health, The Hong Kong Polytechnic University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/Jingyin Lin/PKEcho-Net. |
| Open Datasets | Yes | We evaluated our method on two public echocardiography video datasets: Echo Net-Dynamic (Ouyang et al. 2020) and CAMUS (Leclerc et al. 2019) datasets. |
| Dataset Splits | Yes | We split the training set, validation set, and test set with a ratio of 7:1:2, where four kinds of data augmentations are used to enrich the video data diversity for training |
| Hardware Specification | Yes | Efficiency comparison with the state-of-the-art methods on one RTX 3090 GPU at 320 x 320 resolution. |
| Software Dependencies | No | The paper states 'We implemented our method with the Py Torch framework' but does not specify version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We trained our model for 50 epochs with a poly strategy, where the learning rate is multiplied by (1 iter itermax )0.9 for each iteration with an initial learning rate of 1e-3 for all experiments. We set batchsize = 8 and an Adam optimizer (Kingma and Ba 2014) is also used to accelerate the convergence. |